Top Senior Data & Analytics Jobs in Indianapolis, IN

The Senior Analyst will support product engineering efforts by analyzing digital solutions, enhancing user experiences, and collaborating across teams to deliver insights and improvements.

Lead and grow a software engineering team focused on RISC-V software ecosystem, collaborating with teams to establish RISC-V as the standard.

The Data SME will provide strategic guidance on data management and analytics, ensuring data quality and governance while collaborating with global teams to improve data solutions.

The Senior Data Analyst will develop advanced Power BI dashboards, perform complex analyses using SQL and Python, mentor junior analysts, and ensure data quality governance.

The Senior Database Analyst will optimize a legacy SQL Server environment and design a modern platform using PostgreSQL and ClickHouse. Responsibilities include auditing server performance, designing database schemas, managing multi-tenant databases, and improving data ingestion methods.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

Design, build, and operate production-grade data ingestion pipelines and dbt transformations into a Databricks lakehouse. Establish data quality, monitoring, and observability. Partner with Product and Engineering to enable metrics, dashboards, and longitudinal patient analytics supporting clinical and operational decision-making.